@InProceedings{pmlr-v80-belghazi18a, 
	title = {Mutual Information Neural Estimation}, 
	author = {Belghazi, Mohamed Ishmael and Baratin, Aristide and Rajeshwar, Sai and Ozair, Sherjil and Bengio, Yoshua and Courville, Aaron and Hjelm, Devon}, 
	booktitle = {Proceedings of the 35th International Conference on Machine Learning}, 
	pages = {531--540}, year = {2018}, 
	editor = {Jennifer Dy and Andreas Krause}, 
	volume = {80}, 
	series = {Proceedings of Machine Learning Research}, 
	address = {Stockholmsmässan, Stockholm Sweden}, 
	month = {10--15 Jul}, 
	publisher = {PMLR}, 
	pdf = {http://proceedings.mlr.press/v80/belghazi18a/belghazi18a.pdf}, 
	url = {http://proceedings.mlr.press/v80/belghazi18a.html}, 
	abstract = {We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement the Information Bottleneck, applying it to supervised classification; our results demonstrate substantial improvement in flexibility and performance in these settings.} 
}